---
title: "Building AI-Powered Java Applications with A2A Agents"
description: "Move beyond simple chatbots. Learn how to build Multi-Agent Systems (Agent-to-Agent) in Java using Spring AI to orchestrate complex workflows."
date: "2026-01-22"
author: "Jayesh Jain"
category: "Java"
tags: ["Spring AI", "Java 25", "Multi-Agent Systems", "A2A", "LLMs"]
keywords: "Java AI Agents, Spring AI Multi-Agent, Agent-to-Agent Communication, LangChain4j vs Spring AI, Java LLM Orchestration"
featuredImage: "/blog/building-ai-java-apps-a2a-agents.png"
cta: "Build Enterprise AI."
ctaDescription: "We leverage Spring AI to build secure, scalable, and autonomous agent systems for the enterprise."
---

# Building AI-Powered Java Applications with A2A Agents

## Introduction

In the world of Generative AI, the "Chatbot" phase is over. The "Agent" phase has begun. While Python has dominated AI research, **Java** is claiming its throne in the Enterprise AI space.

Why? because when you move from "toys" to "production systems," you need type safety, concurrency (Virtual Threads), and robust integration patterns.

Today, we explore **Agent-to-Agent (A2A)** interaction: creating specialized AI agents that talk to *each other* to solve complex problems without human intervention.

## What is an A2A System?

A single LLM (Large Language Model) is a generalist. It's okay at everything but great at nothing.
An **A2A System** is a team of specialists.

*   **Manager Agent:** Breaks down a user goal into sub-tasks.
*   **Research Agent:** Has internet access (Search Tool).
*   **Coding Agent:** Has a sandbox environment.
*   **Reviewer Agent:** Critiques the code.

The Manager passes tasks to the Researcher, who passes data to the Coder, who gets critiqued by the Reviewer.

## The Stack: Spring AI

We will use **Spring AI**, the official framework bringing AI capabilities to the Spring ecosystem. It abstracts away the complexity of connecting to OpenAI, Bedrock, or Ollama.

### Step 1: Dependencies

Ensure you are using Spring Boot 3.4+ and Java 25.

```xml
<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-openai-spring-boot-starter</artifactId>
</dependency>
```

### Step 2: Defining the Tools (The Skills)

Agents need tools (Functions) to perform actions. Let's define a tool that allows an agent to "Delegate" work to another agent.

In Spring AI, any Java **Function** can be a tool.

```java
@Configuration
public class AgentTools {

    @Bean
    @Description("Ask the Math Specialist to solve complex calculations")
    public Function<MathRequest, MathResponse> callMathAgent() {
        return request -> {
            // In a real system, this would call another LLM or a specialized service
            System.out.println("DELEGATING TO MATH AGENT: " + request.expression());
            return new MathResponse(evaluateExpression(request.expression()));
        };
    }
}

public record MathRequest(String expression) {}
public record MathResponse(double result) {}
```

### Step 3: The Manager Agent (The Orchestrator)

The Manager Agent is the entry point. It decides *which* specialist to call.

```java
@Service
public class OrchestratorService {

    private final ChatClient chatClient;

    public OrchestratorService(ChatClient.Builder builder) {
        this.chatClient = builder
                .defaultSystem("You are a Project Manager. You do not do work yourself. " +
                             "You delegate tasks to your available tools: 'Math Specialist'.")
                .defaultFunctions("callMathAgent") // Register the tool
                .build();
    }

    public String handleUserRequest(String prompt) {
        // The LLM will decide IF it needs to call the tool based on the prompt
        return chatClient.prompt()
                .user(prompt)
                .call()
                .content();
    }
}
```

### Step 4: Seeing it in Action

**Scenario:** User asks, *"If I invest $10,000 at 5% interest for 10 years, and then take half of it to buy 50 widgets, how much per widget did I pay?"*

1.  **Manager Agent** receives the prompt. "This requires math. I should not hallucinate the answer."
2.  **Manager** generates a Tool Call: **callMathAgent("10000 * (1.05)^10")**.
3.  **Spring AI** executes the Java function **callMathAgent**.
4.  **Math Agent** (Function) calculates **$16,288.95** and returns it.
5.  **Manager** thinks: "Now I need to divide half of $16,288.95 by 50."
6.  **Manager** generates Tool Call: **callMathAgent("(16288.95 / 2) / 50")**.
7.  **Math Agent** returns **162.88**.
8.  **Manager** responds to User: *"You paid roughly $162.88 per widget."*

## Why Java for A2A?

In Python, running 50 concurrent agents can choke the GIL (Global Interpreter Lock). In Java 25, with **Virtual Threads (Project Loom)**, you can spawn **thousands** of autonomous agents in parallel with minimal memory footprint.

```java
// Java 25: Massive Parallelism for Agents
try (var scope = new StructuredTaskScope.ShutdownOnFailure()) {
    var researchTask = scope.fork(() -> researchAgent.investigate(topic));
    var marketTask = scope.fork(() -> marketAgent.analyzePatterns(topic));
    
    scope.join(); // Wait for all agents to finish
    
    var finalReport = synthesizerAgent.combine(researchTask.get(), marketTask.get());
}
```

## Conclusion

Building A2A systems in Java is not just possible; it's often architecturally superior for enterprise workloads. By leveraging **Spring AI** for the LLM abstraction and **Java 25** for the concurrency, you can build Multi-Agent Systems that are robust, type-safe, and ready for production.